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Found 173 Skills
Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Requires Trigger.dev MCP tools for run analysis.
Audit your Claude Code setup for token waste and context bloat. Use when the user says "audit my context", "check my settings", "why is Claude so slow", "token optimization", "context audit", or runs /context-audit. Starts by running /context to see real overhead, then audits MCP servers, CLAUDE.md rules, skills, settings, and file permissions. Returns a health score with specific fixes.
Cost-conscious Claude Code mode. Reduces output tokens 40-70% and overall costs 30-60% by enforcing concise responses, smart model routing, and efficient workflow patterns. Keeps full technical accuracy. Activate with /cost-mode or "enable cost mode". Auto-triggers on mentions of budget, cost, tokens, or spending.
Audit token waste across agent systems (Claude Code, OpenClaw, Hermes, OpenCode). Detect idle burns, model misrouting, and config bloat with dollar savings.
Compass AI integration. Manage data, records, and automate workflows. Use when the user wants to interact with Compass AI data.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.
Analyze and optimize AWS costs with recommendations for Reserved Instances, right-sizing, and resource cleanup. Use when reducing AWS spending, analyzing costs, or optimizing cloud infrastructure expenses.
Deploy GPU workloads to RunPod serverless and pods - vLLM endpoints, A100/H100 setup, scale-to-zero, cost optimization. Use when: deploy to RunPod, GPU serverless, vLLM endpoint, scale to zero, A100 deployment, H100 setup, serverless handler, GPU cost optimization.
Redis semantic caching for LLM applications. Use when implementing vector similarity caching, optimizing LLM costs through cached responses, or building multi-level cache hierarchies.
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.